The present invention generally relates to dot region discriminating methods, and more particularly to a dot region discriminating method for automatically discriminating a dot region from a line region within an image.
In copying machines and facsimile machines, an image which is copied, transmitted or received may be a composite image in which a dot image and a line image coexist. The dot image refers to a photograph, a picture or the like which is described by dots, while the line image refers to a character or the like which is described by lines. In order to improve the quality of the copied, transmitted or received image, it is desirable to carry out a process of eliminating the moire with respect to the dot region such as a dot photograph and to carry out a sharpening process with respect to the line region such as a character. In addition, when transmitting the composite image, it is desirable from the point of view of improving the compression rate that a coding process is carried out after processes appropriate for characteristics of various regions of the image are carried out.
As a method of discriminating the dot region from the image, there is a method proposed by H. Ueno, "Reproduction of Dot Photograph by Dot Printer", Oki Denki Research and Development, No. 132, Vol. 53, No. 4, Oct. 1986. This method proposed by H. Ueno will hereinafter be referred to as the Ueno method.
FIG. 1 shows the basic processes of the Ueno method. An input image signal is generated by making a raster scan of a document image in a step 1. This input image signal is a digital multilevel signal. Then, in a step 2, a difference signal is generated from the input image signal by calculating a density difference of the brightness between each two mutually adjacent picture elements along the direction of the raster scan. An extreme point (peak or valley) of the density change of the picture elements is detected from the difference signal in a step 3. The extreme point is detected when one of the following conditions are satisfied.
Condition (i): The picture element is regarded as the extreme point when the sign of the difference signal changes before and after this picture element as shown in FIG. 2A.
Condition (ii): The picture element is regarded as the extreme point when the sign of the difference signal changes before and after this picture element which has a difference value of zero as shown in FIG. 2B.
Condition (iii): The picture element is regarded as the extreme point when a distance between this picture element and a previous extreme point becomes a predetermined threshold value L.sub.th0.
Then, a dot region is detected in a step 4 based on the extreme points detected in the step 3. The dot region is detected when the following two conditions are simultaneously satisfied.
Condition (iv): A distance L(i) between two successive extreme points is within threshold values L.sub.th1 and L.sub.th2, that is, L.sub.th1 &lt;L(i)&lt;L.sub.th2.
Condition (v): A difference between the distance L(i) at the present position and a distance L(i-1) at a previous position is within a threshold value L.sub.th3, that is, .vertline.L(i)-L(i-1).vertline..ltoreq.L.sub.th3.
Finally, in a step 5, an output image signal which corresponds to the line image or the dot image is generated depending on the discrimination result, that is, whether or not the dot region is detected in the step 4.
The Ueno method discriminates the dot region on a premise that the peaks and valleys of the density level of the dot region occurs regularly. But in general, a large number of extreme points exist in regions such as character regions and continuous gradation photograph regions which are other than the dot region. For this reason, the Ueno method suffers a problem in that the dot region cannot be discriminated with a high accuracy.
In addition, the Ueno method detects the dot region by making a one dimensional comparison of the two successive picture elements which are arranged on the raster scan line. As a result, the distance L(i) between the two successive extreme points becomes long in cases where the dot region occupies a large or small area of the document image and in a case where a skew of the document occurs and a screen angle shifts from a horizontal direction. In such cases, there is a problem in that it is extremely difficult to discriminate the dot region from other regions of the document image such as the character region.